The paper presents a data‑driven framework for optimal sensor placement in aerosol monitoring across the UK. It integrates a convolutional Gaussian neural process into the DeepSensor optimisation framework, using genetic optimisation with lazy evaluation and caching to cut computational time by 60% while matching the performance of a greedy solver. The approach achieves a 22% improvement in Joint NLL for Joint‑MI‑driven genetic placement after selecting five stations, and shows that model‑based criteria align with oracle benchmarks under certain meteorological conditions.
arXiv:2607. 11896v1 Announce Type: cross Abstract: Forecasting particulate matter (PM10) requires both station-scale accuracy and continuous spatial fields, especially during severe dust storms.
arXiv:2607. 19381v1 Announce Type: new Abstract: Air pollution causes an estimated 7.
The paper investigates whether generative deep learning models can nowcast aerosol particle size distributions (PSDs) from routine PM2.5 measurements and meteorological data. Using data from two Finnish stations—Hyytiälä (boreal forest) and Kumpula (urban)—the authors benchmark six generative models against deterministic and climatological baselines, finding that flow matching, especially its guided variant, achieves the lowest continuous ranked probability score (CRPS). Incorporating a physics-based constraint derived from a reduced aerosol general dynamic equation further improves performance, reducing CRPS by 5.5–11.5% at the training site and improving transferability between sites.
The paper presents a six‑month dataset of indoor air‑quality measurements from low‑cost and reference sensors, collected at five locations with contextual metadata. It defines four evaluation scenarios—reference‑efficient, location‑transfer, long‑term drift, and event‑conditioned—to test spatial generalization and robustness to distribution shifts. A lightweight temporal model that compresses input windows and fuses residual temporal and feature information achieves strong calibration performance across all scenarios while keeping edge‑inference cost low.
arXiv:2608. 09775v1 Announce Type: new Abstract: Accurate air quality forecasting is essential for public health and urban environmental management, but remains challenging because pollutant channels differ in periodicity and distribution drift, while their concentration trajectories contain both multi-scale dependencies and rapid changes.
arXiv:2607. 17037v1 Announce Type: new Abstract: High-resolution atmospheric data are required to resolve mesoscale and localized meteorological structures, however such datasets remain limited in many regions of the world.
arXiv:2510. 22863v2 Announce Type: replace-cross Abstract: Reliable long-term forecasting of PM2.
Poor air quality in urban areas is driven by a complex chain of processes and presents a significant public health concern. To better understand and control the mechanisms that determine air quality, cities deploy networks of measurement stations, and launch initiatives for collecting denser data about the concentration of pollutants in the atmosphere.
This study presents a new mobile‑sensing dataset from Surat, India, capturing PM2.5 concentrations along with meteorological and land‑use variables. The authors model the data as a graph using two node‑definition strategies—uniform segmentation and DBSCAN clustering—and introduce a Spatially Attentive Graph Neural Network (SA‑GNN) that combines cluster‑specific GRUs with a Graph Attention Network to forecast fine‑grained, short‑term PM2.5 levels. SA‑GNN outperforms traditional LSTM, RNN, GRU, and ANN baselines, achieving an R² of 0.95, RMSE of 6.8, and MAE of 4.2 µg/m³ on the dataset.
arXiv:2607. 25687v1 Announce Type: cross Abstract: Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making.
arXiv:2607. 20778v1 Announce Type: new Abstract: Weather forecasting foundation models (FMs) are increasingly fine-tuned to predict air quality, offering fast global pollution forecasts at lower computational cost than conventional chemical transport models.
arXiv:2608. 11839v1 Announce Type: new Abstract: Poor air quality in urban areas is driven by a complex chain of processes and presents a significant public health concern.